3D Food Printing Applications Related to Dysphagia: A Narrative Review
Bibliographic record
Abstract
Dysphagia is a condition in which the swallowing mechanism is impaired. It is most often a result of a stroke. Dysphagia has serious consequences, including choking and aspiration pneumonia, which can both be fatal. The population that is most affected by it is the elderly. Texture-modified diets are part of the treatment plan for dysphagia. This bland, restrictive diet often contributes to malnutrition in patients with dysphagia. Both energy and protein intake are of concern, which is especially worrying, as it affects the elderly. Making texture-modified diets more appealing is one method to increase food intake. As a recent technology, 3D food printing has great potential to increase the appeal of textured foods. With extrusion-based printing, both protein and vegetable products have already been 3D printed that fit into the texture categories provided by the International Dysphagia Diet Standardization Initiative. Another exciting advancement is 4D food printing which could make foods even more appealing by incorporating color change and aroma release following a stimulus. The ultra-processed nature of 3D-printed foods is of nutritional concern since this affects the digestion of the food and negatively affects the gut microbiome. There are mitigating strategies to this issue, including the addition of hydrocolloids that increase stomach content viscosity and the addition of probiotics. Therefore, 3D food printing is an improved method for the production of texture-modified diets that should be further explored.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".